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        data-chapter-title="DataFrame查询1-整体"
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                        数据分析
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                            <b>1.</b>
                        
                        Python数据分析内容
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                            <b>2.</b>
                        
                        python数据分析环境和工具
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                            <b>2.1.</b>
                        
                        Python数据课程 软件和环境安装
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                            <b>2.2.</b>
                        
                        python发行版
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                            <b>2.3.</b>
                        
                        交互式编辑器-JupyterNotebook
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                            <b>2.3.1.</b>
                        
                        Jupyter-notebook拓展应用
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                            <b>2.4.</b>
                        
                        包和环境管理器：conda
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                            <b>2.4.1.</b>
                        
                        pip和Virtualenv
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                            <b>2.5.</b>
                        
                        标记语言：Markdown
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                            <b>2.5.1.</b>
                        
                        Markdown语法
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                            <b>2.5.2.</b>
                        
                        文档管理工具-Gitbook
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                            <b>3.</b>
                        
                        数据分析库-Pandas
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                            <b>3.1.</b>
                        
                        pandas
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                            <b>3.2.</b>
                        
                        Series
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                            <b>3.3.</b>
                        
                        DataFrame对象-创建
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            <span><b>4.</b> 数据分析库的操作</span>
            
            
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                            <b>4.1.</b>
                        
                        DataFrame查询1-整体
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                            <b>4.2.</b>
                        
                        DataFrame查询2-专用查询
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                            <b>4.3.</b>
                        
                        DataFrame查询3-专有查询：过滤查询
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                            <b>4.4.</b>
                        
                        Pandas对象的数据操作：增删改查
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                            <b>4.4.1.</b>
                        
                        Pandas数据操作：其他操作
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                            <b>4.4.2.</b>
                        
                        Pandas数据存取
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                            <b>4.4.3.</b>
                        
                        Pandas数据运算
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                            <b>4.4.3.1.</b>
                        
                        Pandas数据运算-拓展
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        <li class="chapter " data-level="4.4.4" data-path="数据分析库的操作/8Pandas分组聚合1.html">
            
                
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                            <b>4.4.4.</b>
                        
                        Pandas分组聚合1
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                            <b>4.4.5.</b>
                        
                        Pandas分组聚合2
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                            <b>4.4.6.</b>
                        
                        Pandas数据规整-清理
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                            <b>4.4.7.</b>
                        
                        Pandas数据规整-转换
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                            <b>4.4.7.1.</b>
                        
                        离散化和面元划分
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        <li class="chapter " data-level="4.4.8" data-path="数据分析库的操作/16Pandas数据规整-合并.html">
            
                
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                            <b>4.4.8.</b>
                        
                        Pandas数据规整-合并
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                            <b>4.5.</b>
                        
                        Pandas数据规整-重塑和轴向旋转
                    </a>
            
            
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        <li class="chapter " data-level="4.5.1" data-path="数据分析库的操作/13.1透视表和交叉表.html">
            
                
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                            <b>4.5.1.</b>
                        
                        透视表和交叉表
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        <li class="chapter " data-level="5" data-path="Python可视化/绘图库-Matplotlib.html">
            
                
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                            <b>5.</b>
                        
                        Python可视化
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        <li class="chapter " data-level="5.1" data-path="Python可视化/绘图库-Matplotlib/Matplotlib常见图表.html">
            
                
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                            <b>5.1.</b>
                        
                        基础：Matplotlib常见图表
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                            <b>5.1.1.</b>
                        
                        Matplotlib常见设置和操作
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                            <b>5.2.</b>
                        
                        提升：绘图区域
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        <li class="chapter " data-level="5.3" data-path="Python可视化/绘图库-Matplotlib/2Matplotlib-图像组件.html">
            
                
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                            <b>5.3.</b>
                        
                        提升：绘图组件
                    </a>
            
            
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        <li class="chapter " data-level="5.4" data-path="Python可视化/绘图库-Matplotlib/3Matplotlib-高级绘图.html">
            
                
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                            <b>5.4.</b>
                        
                        拓展：高级绘图
                    </a>
            
            
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        <li class="chapter " data-level="5.5" data-path="Python可视化/绘图库-Matplotlib/4数学计算展示图像.html">
            
                
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                            <b>5.5.</b>
                        
                        拓展：数学计算展示图像
                    </a>
            
            
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        <li class="chapter " data-level="5.6" data-path="Python可视化/绘图库-Matplotlib/5注意事项.html">
            
                
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                            <b>5.6.</b>
                        
                        拓展：注意事项
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        <li class="chapter " data-level="5.7" data-path="Python可视化/绘图库-Matplotlib/6pylab.html">
            
                
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                            <b>5.7.</b>
                        
                        拓展：pylab
                    </a>
            
            
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                            <b>6.</b>
                        
                        数据分析必备知识点
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        <li class="chapter " data-level="7" data-path="数据分析必备知识点/数据分析流程.html">
            
                
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                            <b>7.</b>
                        
                        数据分析流程
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                        <h2 id="dataframe&#x67E5;&#x8BE2;1&#x6574;&#x4F53;">DataFrame&#x67E5;&#x8BE2;1-&#x6574;&#x4F53;</h2>
<hr>
<pre><code class="lang-python"><span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np
<span class="hljs-keyword">import</span> pandas <span class="hljs-keyword">as</span> pd
</code></pre>
<p>&#x6570;&#x636E;</p>
<pre><code class="lang-python">a = pd.DataFrame([
    [<span class="hljs-string">&apos;&#x5C0F;&#x660E;&apos;</span>,<span class="hljs-string">&apos;male&apos;</span>,<span class="hljs-number">18</span>,<span class="hljs-number">170</span>,<span class="hljs-number">60</span>,<span class="hljs-string">&apos;&#x5317;&#x4EAC;&#x6D77;&#x6DC0;&apos;</span>,<span class="hljs-number">61</span>],
    [<span class="hljs-string">&apos;&#x5C0F;&#x534E;&apos;</span>,<span class="hljs-string">&apos;female&apos;</span>,<span class="hljs-number">28</span>,<span class="hljs-number">160</span>,<span class="hljs-number">50</span>,<span class="hljs-string">&apos;&#x4E0A;&#x6D77;&#x9759;&#x5B89;&apos;</span>,<span class="hljs-number">74</span>],
    [<span class="hljs-string">&apos;&#x5C0F;&#x7EA2;&apos;</span>,<span class="hljs-string">&apos;female&apos;</span>,<span class="hljs-number">22</span>,<span class="hljs-number">175</span>,<span class="hljs-number">64</span>,<span class="hljs-string">&apos;&#x5E7F;&#x5DDE;&#x5929;&#x6CB3;&apos;</span>,<span class="hljs-number">59</span>],
    [<span class="hljs-string">&apos;&#x5C0F;&#x9751;&apos;</span>,<span class="hljs-string">&apos;male&apos;</span>,<span class="hljs-number">31</span>,<span class="hljs-number">182</span>,<span class="hljs-number">80</span>,<span class="hljs-string">&apos;&#x6DF1;&#x5733;&#x5357;&#x5C71;&apos;</span>,<span class="hljs-number">82</span>],
    [<span class="hljs-string">&apos;&#x5C0F;&#x5170;&apos;</span>,<span class="hljs-string">&apos;female&apos;</span>,<span class="hljs-number">25</span>,<span class="hljs-number">165</span>,<span class="hljs-number">55</span>,<span class="hljs-string">&apos;&#x676D;&#x5DDE;&#x897F;&#x6E56;&apos;</span>,<span class="hljs-number">98</span>],
],
     index=[<span class="hljs-number">1</span>,<span class="hljs-number">2</span>,<span class="hljs-number">3</span>,<span class="hljs-number">4</span>,<span class="hljs-number">5</span>],
    columns=[<span class="hljs-string">&apos;name&apos;</span>,<span class="hljs-string">&apos;sex&apos;</span>,<span class="hljs-string">&apos;age&apos;</span>,<span class="hljs-string">&apos;heigh&apos;</span>,<span class="hljs-string">&apos;weight&apos;</span>,<span class="hljs-string">&apos;address&apos;</span>,<span class="hljs-string">&apos;grade&apos;</span>]
)
a
</code></pre>
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>name</th>
      <th>sex</th>
      <th>age</th>
      <th>heigh</th>
      <th>weight</th>
      <th>address</th>
      <th>grade</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>1</th>
      <td>&#x5C0F;&#x660E;</td>
      <td>male</td>
      <td>18</td>
      <td>170</td>
      <td>60</td>
      <td>&#x5317;&#x4EAC;&#x6D77;&#x6DC0;</td>
      <td>61</td>
    </tr>
    <tr>
      <th>2</th>
      <td>&#x5C0F;&#x534E;</td>
      <td>female</td>
      <td>28</td>
      <td>160</td>
      <td>50</td>
      <td>&#x4E0A;&#x6D77;&#x9759;&#x5B89;</td>
      <td>74</td>
    </tr>
    <tr>
      <th>3</th>
      <td>&#x5C0F;&#x7EA2;</td>
      <td>female</td>
      <td>22</td>
      <td>175</td>
      <td>64</td>
      <td>&#x5E7F;&#x5DDE;&#x5929;&#x6CB3;</td>
      <td>59</td>
    </tr>
    <tr>
      <th>4</th>
      <td>&#x5C0F;&#x9751;</td>
      <td>male</td>
      <td>31</td>
      <td>182</td>
      <td>80</td>
      <td>&#x6DF1;&#x5733;&#x5357;&#x5C71;</td>
      <td>82</td>
    </tr>
    <tr>
      <th>5</th>
      <td>&#x5C0F;&#x5170;</td>
      <td>female</td>
      <td>25</td>
      <td>165</td>
      <td>55</td>
      <td>&#x676D;&#x5DDE;&#x897F;&#x6E56;</td>
      <td>98</td>
    </tr>
  </tbody>
</table>
</div>



<h2 id="&#x67E5;&#x8BE2;">&#x67E5;&#x8BE2;</h2>
<h3 id="&#x5E38;&#x7528;&#x5C5E;&#x6027;">&#x5E38;&#x7528;&#x5C5E;&#x6027;</h3>
<p>a.shape  # &#x8868;&#x683C;&#x5F62;&#x72B6; &#x884C;&#x6570; &#x5217;&#x6570;</p>
<p>a.dtypes  # &#x5217;&#x6570;&#x636E;&#x7C7B;&#x578B;</p>
<p>a.index  # &#x884C;&#x7D22;&#x5F15;</p>
<p>a.columns  # &#x5217;&#x7D22;&#x5F15;</p>
<p>a.values  # &#x5BF9;&#x8C61;&#x503C;&#xFF0C;&#x4E8C;&#x7EF4;ndarray&#x6570;&#x7EC4;</p>
<h3 id="&#x8868;&#x683C;&#x5F62;&#x72B6;&#xFF08;&#x51E0;&#x884C;&#x51E0;&#x5217;&#xFF09;">&#x8868;&#x683C;&#x5F62;&#x72B6;&#xFF08;&#x51E0;&#x884C;&#x51E0;&#x5217;&#xFF09;</h3>
<pre><code class="lang-python">a.shape
</code></pre>
<pre><code>(5, 7)
</code></pre><h3 id="&#x6570;&#x636E;&#x7C7B;&#x578B;">&#x6570;&#x636E;&#x7C7B;&#x578B;</h3>
<pre><code class="lang-python">a.dtypes
</code></pre>
<pre><code>name        object
sex         object
age          int64
heigh      float64
weight       int64
address     object
grade        int64
dtype: object
</code></pre><pre><code class="lang-python">a.shape[<span class="hljs-number">0</span>],a.shape[<span class="hljs-number">1</span>]
</code></pre>
<pre><code>(5, 7)
</code></pre><p>&#x6570;&#x636E;</p>
<pre><code class="lang-python">a_values = [
    [<span class="hljs-string">&apos;&#x5C0F;&#x660E;&apos;</span>,<span class="hljs-string">&apos;male&apos;</span>,<span class="hljs-number">18</span>,<span class="hljs-number">170.0</span>,<span class="hljs-number">60</span>,<span class="hljs-string">&apos;&#x5317;&#x4EAC;&#x6D77;&#x6DC0;&apos;</span>,<span class="hljs-number">61</span>],  <span class="hljs-comment">#&#x5982;&#x679C;&#x8EAB;&#x9AD8;&#x8FD9;&#x4E00;&#x5217; &#x4E00;&#x4E2A;&#x6570;&#x636E;&#x6539;&#x4E3A;&#x6D6E;&#x70B9;&#x578B;&#xFF0C;&#x90A3;&#x4E48;&#x6574;&#x5217;&#x7684;&#x6570;&#x636E;&#x7C7B;&#x578B;&#x90FD;&#x4F1A;&#x88AB;&#x5F71;&#x54CD;&#x4E3A;&#x6D6E;&#x70B9;&#x578B;&#x3002;</span>
    [<span class="hljs-string">&apos;&#x5C0F;&#x534E;&apos;</span>,<span class="hljs-string">&apos;female&apos;</span>,<span class="hljs-number">28</span>,<span class="hljs-number">160</span>,<span class="hljs-number">50</span>,<span class="hljs-string">&apos;&#x4E0A;&#x6D77;&#x9759;&#x5B89;&apos;</span>,<span class="hljs-number">74</span>],
    [<span class="hljs-string">&apos;&#x5C0F;&#x7EA2;&apos;</span>,<span class="hljs-string">&apos;female&apos;</span>,<span class="hljs-number">22</span>,<span class="hljs-number">175</span>,<span class="hljs-number">64</span>,<span class="hljs-string">&apos;&#x5E7F;&#x5DDE;&#x5929;&#x6CB3;&apos;</span>,<span class="hljs-number">59</span>],
    [<span class="hljs-string">&apos;&#x5C0F;&#x9751;&apos;</span>,<span class="hljs-string">&apos;male&apos;</span>,<span class="hljs-number">31</span>,<span class="hljs-number">182</span>,<span class="hljs-number">80</span>,<span class="hljs-string">&apos;&#x6DF1;&#x5733;&#x5357;&#x5C71;&apos;</span>,<span class="hljs-number">82</span>],
    [<span class="hljs-string">&apos;&#x5C0F;&#x5170;&apos;</span>,<span class="hljs-string">&apos;female&apos;</span>,<span class="hljs-number">25</span>,<span class="hljs-number">165</span>,<span class="hljs-number">55</span>,<span class="hljs-string">&apos;&#x676D;&#x5DDE;&#x897F;&#x6E56;&apos;</span>,<span class="hljs-number">98</span>],
]

a = pd.DataFrame(
    a_values,
    index=[<span class="hljs-number">1</span>,<span class="hljs-number">2</span>,<span class="hljs-number">3</span>,<span class="hljs-number">4</span>,<span class="hljs-number">5</span>],
    columns=[<span class="hljs-string">&apos;name&apos;</span>,<span class="hljs-string">&apos;sex&apos;</span>,<span class="hljs-string">&apos;age&apos;</span>,<span class="hljs-string">&apos;heigh&apos;</span>,<span class="hljs-string">&apos;weight&apos;</span>,<span class="hljs-string">&apos;address&apos;</span>,<span class="hljs-string">&apos;grade&apos;</span>]
)
a
</code></pre>
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>name</th>
      <th>sex</th>
      <th>age</th>
      <th>heigh</th>
      <th>weight</th>
      <th>address</th>
      <th>grade</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>1</th>
      <td>&#x5C0F;&#x660E;</td>
      <td>male</td>
      <td>18</td>
      <td>170.0</td>
      <td>60</td>
      <td>&#x5317;&#x4EAC;&#x6D77;&#x6DC0;</td>
      <td>61</td>
    </tr>
    <tr>
      <th>2</th>
      <td>&#x5C0F;&#x534E;</td>
      <td>female</td>
      <td>28</td>
      <td>160.0</td>
      <td>50</td>
      <td>&#x4E0A;&#x6D77;&#x9759;&#x5B89;</td>
      <td>74</td>
    </tr>
    <tr>
      <th>3</th>
      <td>&#x5C0F;&#x7EA2;</td>
      <td>female</td>
      <td>22</td>
      <td>175.0</td>
      <td>64</td>
      <td>&#x5E7F;&#x5DDE;&#x5929;&#x6CB3;</td>
      <td>59</td>
    </tr>
    <tr>
      <th>4</th>
      <td>&#x5C0F;&#x9751;</td>
      <td>male</td>
      <td>31</td>
      <td>182.0</td>
      <td>80</td>
      <td>&#x6DF1;&#x5733;&#x5357;&#x5C71;</td>
      <td>82</td>
    </tr>
    <tr>
      <th>5</th>
      <td>&#x5C0F;&#x5170;</td>
      <td>female</td>
      <td>25</td>
      <td>165.0</td>
      <td>55</td>
      <td>&#x676D;&#x5DDE;&#x897F;&#x6E56;</td>
      <td>98</td>
    </tr>
  </tbody>
</table>
</div>



<p>&#x8868;&#x683C;&#x503C;</p>
<pre><code class="lang-python">
a.values  <span class="hljs-comment">#&#x4E8C;&#x7EF4;&#x6570;&#x7EC4;</span>
</code></pre>
<pre><code>array([[&apos;&#x5C0F;&#x660E;&apos;, &apos;male&apos;, 18, 170.0, 60, &apos;&#x5317;&#x4EAC;&#x6D77;&#x6DC0;&apos;, 61],
       [&apos;&#x5C0F;&#x534E;&apos;, &apos;female&apos;, 28, 160.0, 50, &apos;&#x4E0A;&#x6D77;&#x9759;&#x5B89;&apos;, 74],
       [&apos;&#x5C0F;&#x7EA2;&apos;, &apos;female&apos;, 22, 175.0, 64, &apos;&#x5E7F;&#x5DDE;&#x5929;&#x6CB3;&apos;, 59],
       [&apos;&#x5C0F;&#x9751;&apos;, &apos;male&apos;, 31, 182.0, 80, &apos;&#x6DF1;&#x5733;&#x5357;&#x5C71;&apos;, 82],
       [&apos;&#x5C0F;&#x5170;&apos;, &apos;female&apos;, 25, 165.0, 55, &apos;&#x676D;&#x5DDE;&#x897F;&#x6E56;&apos;, 98]], dtype=object)
</code></pre><pre><code class="lang-python">a.values[<span class="hljs-number">0</span>]
</code></pre>
<pre><code>array([&apos;&#x5C0F;&#x660E;&apos;, &apos;male&apos;, 18, 170.0, 60, &apos;&#x5317;&#x4EAC;&#x6D77;&#x6DC0;&apos;, 61], dtype=object)
</code></pre><pre><code class="lang-python">a.values[<span class="hljs-number">0</span>][<span class="hljs-number">0</span>]
</code></pre>
<pre><code>&apos;&#x5C0F;&#x660E;&apos;
</code></pre><p>&#x8868;&#x683C;&#x7684;&#x884C;&#x7D22;&#x5F15;</p>
<pre><code class="lang-python">a.index
a.index.values
</code></pre>
<pre><code>array([1, 2, 3, 4, 5], dtype=int64)
</code></pre><pre><code class="lang-python">type(a.index),type(a.index.values)
</code></pre>
<pre><code>(pandas.core.indexes.numeric.Int64Index, numpy.ndarray)
</code></pre><p>&#x8868;&#x683C;&#x5217;&#x7D22;&#x5F15;</p>
<pre><code class="lang-python">a.columns
</code></pre>
<pre><code>Index([&apos;name&apos;, &apos;sex&apos;, &apos;age&apos;, &apos;heigh&apos;, &apos;weight&apos;, &apos;address&apos;, &apos;grade&apos;], dtype=&apos;object&apos;)
</code></pre><pre><code class="lang-python">a.columns.values
</code></pre>
<pre><code>array([&apos;name&apos;, &apos;sex&apos;, &apos;age&apos;, &apos;heigh&apos;, &apos;weight&apos;, &apos;address&apos;, &apos;grade&apos;],
      dtype=object)
</code></pre><pre><code class="lang-python">a.columns.values[<span class="hljs-number">0</span>]
</code></pre>
<pre><code>&apos;name&apos;
</code></pre><h2 id="&#x6574;&#x4F53;&#x6570;&#x636E;&#x60C5;&#x51B5;">&#x6574;&#x4F53;&#x6570;&#x636E;&#x60C5;&#x51B5;</h2>
<ul>
<li>a.info() &#x6574;&#x4F53;&#x4FE1;&#x606F;&#xFF0C;&#x67E5;&#x770B;&#x6570;&#x636E;&#x662F;&#x5426;&#x5F02;&#x5E38;</li>
<li>a,describe() &#x6574;&#x4F53;&#x7EDF;&#x8BA1;&#x6307;&#x6807;</li>
<li>a.head()&#x524D;5&#x884C;</li>
<li>a.taill() &#x540E;5&#x884C;</li>
</ul>
<pre><code class="lang-python">a
</code></pre>
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>name</th>
      <th>sex</th>
      <th>age</th>
      <th>heigh</th>
      <th>weight</th>
      <th>address</th>
      <th>grade</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>1</th>
      <td>&#x5C0F;&#x660E;</td>
      <td>male</td>
      <td>18</td>
      <td>170.0</td>
      <td>60</td>
      <td>&#x5317;&#x4EAC;&#x6D77;&#x6DC0;</td>
      <td>61</td>
    </tr>
    <tr>
      <th>2</th>
      <td>&#x5C0F;&#x534E;</td>
      <td>female</td>
      <td>28</td>
      <td>160.0</td>
      <td>50</td>
      <td>&#x4E0A;&#x6D77;&#x9759;&#x5B89;</td>
      <td>74</td>
    </tr>
    <tr>
      <th>3</th>
      <td>&#x5C0F;&#x7EA2;</td>
      <td>female</td>
      <td>22</td>
      <td>175.0</td>
      <td>64</td>
      <td>&#x5E7F;&#x5DDE;&#x5929;&#x6CB3;</td>
      <td>59</td>
    </tr>
    <tr>
      <th>4</th>
      <td>&#x5C0F;&#x9751;</td>
      <td>male</td>
      <td>31</td>
      <td>182.0</td>
      <td>80</td>
      <td>&#x6DF1;&#x5733;&#x5357;&#x5C71;</td>
      <td>82</td>
    </tr>
    <tr>
      <th>5</th>
      <td>&#x5C0F;&#x5170;</td>
      <td>female</td>
      <td>25</td>
      <td>165.0</td>
      <td>55</td>
      <td>&#x676D;&#x5DDE;&#x897F;&#x6E56;</td>
      <td>98</td>
    </tr>
  </tbody>
</table>
</div>



<p>&#x8868;&#x683C;&#x6574;&#x4F53;&#x4FE1;&#x606F;&#xFF0C;&#x4E00;&#x822C;&#x7528;&#x4E8E;&#x67E5;&#x770B;&#x8868;&#x683C;&#x6570;&#x636E;&#x662F;&#x5426;&#x6709;&#x5F02;&#x5E38;</p>
<ul>
<li>&#x662F;&#x5426;&#x6709;&#x7F3A;&#x5931;&#x503C;</li>
<li>&#x5217;&#x8868;&#x6570;&#x636E;&#x7C7B;&#x578B;&#x662F;&#x5426;&#x6B63;&#x5E38;</li>
</ul>
<pre><code class="lang-python">a.info()   <span class="hljs-comment">#&#x53EF;&#x4EE5;&#x770B;&#x51FA;&#x5217;&#x7684;&#x6570;&#x636E;&#x6709;&#x6CA1;&#x6709;&#x7F3A;&#x5931;&#xFF0C;&#x6839;&#x636E;&#x7ED3;&#x679C;&#x6765;&#x8865;&#x5168;&#x6570;&#x636E;&#x3002;</span>
</code></pre>
<pre><code>&lt;class &apos;pandas.core.frame.DataFrame&apos;&gt;
Int64Index: 5 entries, 1 to 5
Data columns (total 7 columns):
name       5 non-null object
sex        5 non-null object
age        5 non-null int64
heigh      5 non-null float64
weight     5 non-null int64
address    5 non-null object
grade      5 non-null int64
dtypes: float64(1), int64(3), object(3)
memory usage: 320.0+ bytes
</code></pre><h3 id="&#x8868;&#x683C;&#x5FEB;&#x901F;&#x7EFC;&#x5408;&#x7EDF;&#x8BA1;&#x6307;&#x6807;">&#x8868;&#x683C;&#x5FEB;&#x901F;&#x7EFC;&#x5408;&#x7EDF;&#x8BA1;&#x6307;&#x6807;</h3>
<p>&#x7528;&#x6765;&#x67E5;&#x770B;&#x6570;&#x636E;&#x7684;&#x6574;&#x4F53;&#x7EDF;&#x8BA1;&#x60C5;&#x51B5;</p>
<ul>
<li><p>count&#xFF0C;&#x8BA1;&#x6570;</p>
</li>
<li><p>mean &#x5E73;&#x5747;&#x503C;</p>
</li>
<li><p>std &#x6807;&#x51C6;&#x5DEE;</p>
</li>
<li><p>&#x56DB;&#x5206;&#x4F4D;&#x6570;
 &#x56DB;&#x5206;&#x4F4D;&#x6570;&#xFF08;Quartile&#xFF09;&#x4E5F;&#x79F0;&#x56DB;&#x5206;&#x4F4D;&#x70B9;&#xFF0C;&#x662F;&#x6307;&#x5728;&#x7EDF;&#x8BA1;&#x5B66;&#x4E2D;&#x628A;&#x6240;&#x6709;&#x6570;&#x503C;&#x7531;&#x5C0F;&#x5230;&#x5927;&#x6392;&#x5217;&#x5E76;&#x5206;&#x6210;&#x56DB;&#x7B49;&#x4EFD;&#xFF0C;&#x5904;&#x4E8E;&#x4E09;&#x4E2A;&#x5206;&#x5272;&#x70B9;&#x4F4D;&#x7F6E;&#x7684;&#x6570;&#x503C;</p>
<ul>
<li>min &#x6700;&#x5C0F;&#x503C;&#xFF0C;q1(&#x6307;&#x5904;&#x5728;25%&#x4F4D;&#x7F6E;&#x4E0A;&#x7684;&#x6570;&#x503C;),q2(&#x4E2D;&#x4F4D;&#x6570;),q3(&#x6307;&#x5904;&#x5728;75%&#x4F4D;&#x7F6E;&#x4E0A;&#x7684;&#x6570;&#x503C;),max &#x6700;&#x5927;&#x503C;</li>
</ul>
</li>
</ul>
<pre><code class="lang-python">a.describe()
</code></pre>
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>age</th>
      <th>heigh</th>
      <th>weight</th>
      <th>grade</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>count</th>
      <td>5.000000</td>
      <td>5.000000</td>
      <td>5.000000</td>
      <td>5.000000</td>
    </tr>
    <tr>
      <th>mean</th>
      <td>24.800000</td>
      <td>170.400000</td>
      <td>61.800000</td>
      <td>74.800000</td>
    </tr>
    <tr>
      <th>std</th>
      <td>5.069517</td>
      <td>8.561542</td>
      <td>11.454257</td>
      <td>16.053037</td>
    </tr>
    <tr>
      <th>min</th>
      <td>18.000000</td>
      <td>160.000000</td>
      <td>50.000000</td>
      <td>59.000000</td>
    </tr>
    <tr>
      <th>25%</th>
      <td>22.000000</td>
      <td>165.000000</td>
      <td>55.000000</td>
      <td>61.000000</td>
    </tr>
    <tr>
      <th>50%</th>
      <td>25.000000</td>
      <td>170.000000</td>
      <td>60.000000</td>
      <td>74.000000</td>
    </tr>
    <tr>
      <th>75%</th>
      <td>28.000000</td>
      <td>175.000000</td>
      <td>64.000000</td>
      <td>82.000000</td>
    </tr>
    <tr>
      <th>max</th>
      <td>31.000000</td>
      <td>182.000000</td>
      <td>80.000000</td>
      <td>98.000000</td>
    </tr>
  </tbody>
</table>
</div>



<h4 id="std&#xFF1A;-&#x6807;&#x51C6;&#x5DEE;-&#xFF1A;">std&#xFF1A; &#x6807;&#x51C6;&#x5DEE; &#xFF1A;</h4>
<pre><code>&#x6240;&#x6709;&#x6570;&#x51CF;&#x53BB;&#x5176;&#x5E73;&#x5747;&#x503C;&#x7684;&#x5E73;&#x65B9;&#x548C;&#xFF0C;&#x6240;&#x5F97;&#x7ED3;&#x679C;&#x9664;&#x4EE5;&#x8BE5;&#x7EC4;&#x6570;&#x4E4B;&#x4E2A;&#x6570;&#xFF08;&#x6216;&#x4E2A;&#x6570;&#x51CF;&#x4E00;&#xFF0C;&#x5373;&#x53D8;&#x5F02;&#x6570;&#xFF09;&#xFF0C;&#x518D;&#x628A;&#x6240;&#x5F97;&#x503C;&#x5F00;&#x6839;&#x53F7;&#xFF0C;&#x6240;&#x5F97;&#x4E4B;&#x6570;&#x5C31;&#x662F;&#x8FD9;&#x7EC4;&#x6570;&#x636E;&#x7684;&#x6807;&#x51C6;&#x5DEE;&#x3002;

&#x6807;&#x51C6;&#x5DEE;&#x662F;&#x53CD;&#x6620;&#x4E00;&#x7EC4;&#x6570;&#x636E;&#x79BB;&#x6563;&#x7A0B;&#x5EA6;&#x6700;&#x5E38;&#x7528;&#x7684;&#x4E00;&#x79CD;&#x91CF;&#x5316;&#x5F62;&#x5F0F;&#xFF0C;&#x662F;&#x8868;&#x793A;&#x7CBE;&#x786E;&#x5EA6;&#x7684;&#x91CD;&#x8981;&#x6307;&#x6807;&#x3002;
</code></pre><p>&#x8F93;&#x51FA;&#x8868;&#x683C;&#x7684;&#x524D;5&#x884C;</p>
<pre><code class="lang-python">a.head()
a.head(<span class="hljs-number">3</span>)
</code></pre>
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>name</th>
      <th>sex</th>
      <th>age</th>
      <th>heigh</th>
      <th>weight</th>
      <th>address</th>
      <th>grade</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>1</th>
      <td>&#x5C0F;&#x660E;</td>
      <td>male</td>
      <td>18</td>
      <td>170.0</td>
      <td>60</td>
      <td>&#x5317;&#x4EAC;&#x6D77;&#x6DC0;</td>
      <td>61</td>
    </tr>
    <tr>
      <th>2</th>
      <td>&#x5C0F;&#x534E;</td>
      <td>female</td>
      <td>28</td>
      <td>160.0</td>
      <td>50</td>
      <td>&#x4E0A;&#x6D77;&#x9759;&#x5B89;</td>
      <td>74</td>
    </tr>
    <tr>
      <th>3</th>
      <td>&#x5C0F;&#x7EA2;</td>
      <td>female</td>
      <td>22</td>
      <td>175.0</td>
      <td>64</td>
      <td>&#x5E7F;&#x5DDE;&#x5929;&#x6CB3;</td>
      <td>59</td>
    </tr>
  </tbody>
</table>
</div>



<p>&#x8F93;&#x51FA;&#x8868;&#x683C;&#x7684;&#x540E;5&#x884C;</p>
<pre><code class="lang-python">a.tail()
a.tail(<span class="hljs-number">3</span>)
</code></pre>
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>name</th>
      <th>sex</th>
      <th>age</th>
      <th>heigh</th>
      <th>weight</th>
      <th>address</th>
      <th>grade</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>3</th>
      <td>&#x5C0F;&#x7EA2;</td>
      <td>female</td>
      <td>22</td>
      <td>175.0</td>
      <td>64</td>
      <td>&#x5E7F;&#x5DDE;&#x5929;&#x6CB3;</td>
      <td>59</td>
    </tr>
    <tr>
      <th>4</th>
      <td>&#x5C0F;&#x9751;</td>
      <td>male</td>
      <td>31</td>
      <td>182.0</td>
      <td>80</td>
      <td>&#x6DF1;&#x5733;&#x5357;&#x5C71;</td>
      <td>82</td>
    </tr>
    <tr>
      <th>5</th>
      <td>&#x5C0F;&#x5170;</td>
      <td>female</td>
      <td>25</td>
      <td>165.0</td>
      <td>55</td>
      <td>&#x676D;&#x5DDE;&#x897F;&#x6E56;</td>
      <td>98</td>
    </tr>
  </tbody>
</table>
</div>



<h2 id="&#x5185;&#x5BB9;&#x67E5;&#x8BE2;">&#x5185;&#x5BB9;&#x67E5;&#x8BE2;</h2>
<h3 id="&#x7C7B;&#x5217;&#x8868;&#x5B57;&#x5178;ndarray&#x6570;&#x7EC4;&#x7684;&#x67E5;&#x8BE2;&#x65B9;&#x5F0F;">&#x7C7B;&#x5217;&#x8868;/&#x5B57;&#x5178;/ndarray&#x6570;&#x7EC4;&#x7684;&#x67E5;&#x8BE2;&#x65B9;&#x5F0F;</h3>
<pre><code class="lang-python">a
</code></pre>
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>name</th>
      <th>sex</th>
      <th>age</th>
      <th>heigh</th>
      <th>weight</th>
      <th>address</th>
      <th>grade</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>1</th>
      <td>&#x5C0F;&#x660E;</td>
      <td>male</td>
      <td>18</td>
      <td>170.0</td>
      <td>60</td>
      <td>&#x5317;&#x4EAC;&#x6D77;&#x6DC0;</td>
      <td>61</td>
    </tr>
    <tr>
      <th>2</th>
      <td>&#x5C0F;&#x534E;</td>
      <td>female</td>
      <td>28</td>
      <td>160.0</td>
      <td>50</td>
      <td>&#x4E0A;&#x6D77;&#x9759;&#x5B89;</td>
      <td>74</td>
    </tr>
    <tr>
      <th>3</th>
      <td>&#x5C0F;&#x7EA2;</td>
      <td>female</td>
      <td>22</td>
      <td>175.0</td>
      <td>64</td>
      <td>&#x5E7F;&#x5DDE;&#x5929;&#x6CB3;</td>
      <td>59</td>
    </tr>
    <tr>
      <th>4</th>
      <td>&#x5C0F;&#x9751;</td>
      <td>male</td>
      <td>31</td>
      <td>182.0</td>
      <td>80</td>
      <td>&#x6DF1;&#x5733;&#x5357;&#x5C71;</td>
      <td>82</td>
    </tr>
    <tr>
      <th>5</th>
      <td>&#x5C0F;&#x5170;</td>
      <td>female</td>
      <td>25</td>
      <td>165.0</td>
      <td>55</td>
      <td>&#x676D;&#x5DDE;&#x897F;&#x6E56;</td>
      <td>98</td>
    </tr>
  </tbody>
</table>
</div>



<p> &#x67E5;&#x8BE2;&#x5355;&#x5217;</p>
<pre><code class="lang-python">a[<span class="hljs-string">&apos;name&apos;</span>]
</code></pre>
<pre><code>1    &#x5C0F;&#x660E;
2    &#x5C0F;&#x534E;
3    &#x5C0F;&#x7EA2;
4    &#x5C0F;&#x9751;
5    &#x5C0F;&#x5170;
Name: name, dtype: object
</code></pre><p>&#x67E5;&#x8BE2;&#x591A;&#x5217;</p>
<pre><code class="lang-python">a[[<span class="hljs-string">&apos;name&apos;</span>,<span class="hljs-string">&apos;address&apos;</span>]]
</code></pre>
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>name</th>
      <th>address</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>1</th>
      <td>&#x5C0F;&#x660E;</td>
      <td>&#x5317;&#x4EAC;&#x6D77;&#x6DC0;</td>
    </tr>
    <tr>
      <th>2</th>
      <td>&#x5C0F;&#x534E;</td>
      <td>&#x4E0A;&#x6D77;&#x9759;&#x5B89;</td>
    </tr>
    <tr>
      <th>3</th>
      <td>&#x5C0F;&#x7EA2;</td>
      <td>&#x5E7F;&#x5DDE;&#x5929;&#x6CB3;</td>
    </tr>
    <tr>
      <th>4</th>
      <td>&#x5C0F;&#x9751;</td>
      <td>&#x6DF1;&#x5733;&#x5357;&#x5C71;</td>
    </tr>
    <tr>
      <th>5</th>
      <td>&#x5C0F;&#x5170;</td>
      <td>&#x676D;&#x5DDE;&#x897F;&#x6E56;</td>
    </tr>
  </tbody>
</table>
</div>



<p>&#x67E5;&#x8BE2;&#x5355;&#x884C;</p>
<pre><code class="lang-python">a[<span class="hljs-number">0</span>:<span class="hljs-number">1</span>]
</code></pre>
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>name</th>
      <th>sex</th>
      <th>age</th>
      <th>heigh</th>
      <th>weight</th>
      <th>address</th>
      <th>grade</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>1</th>
      <td>&#x5C0F;&#x660E;</td>
      <td>male</td>
      <td>18</td>
      <td>170.0</td>
      <td>60</td>
      <td>&#x5317;&#x4EAC;&#x6D77;&#x6DC0;</td>
      <td>61</td>
    </tr>
  </tbody>
</table>
</div>



<p>&#x67E5;&#x8BE2;&#x591A;&#x884C;</p>
<pre><code class="lang-python">a[<span class="hljs-number">1</span>:<span class="hljs-number">3</span>]
</code></pre>
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>name</th>
      <th>sex</th>
      <th>age</th>
      <th>heigh</th>
      <th>weight</th>
      <th>address</th>
      <th>grade</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>2</th>
      <td>&#x5C0F;&#x534E;</td>
      <td>female</td>
      <td>28</td>
      <td>160.0</td>
      <td>50</td>
      <td>&#x4E0A;&#x6D77;&#x9759;&#x5B89;</td>
      <td>74</td>
    </tr>
    <tr>
      <th>3</th>
      <td>&#x5C0F;&#x7EA2;</td>
      <td>female</td>
      <td>22</td>
      <td>175.0</td>
      <td>64</td>
      <td>&#x5E7F;&#x5DDE;&#x5929;&#x6CB3;</td>
      <td>59</td>
    </tr>
  </tbody>
</table>
</div>



<p>&#x67E5;&#x8BE2;&#x5355;&#x503C;</p>
<pre><code class="lang-python">a[<span class="hljs-number">0</span>:<span class="hljs-number">1</span>][<span class="hljs-string">&apos;name&apos;</span>]
</code></pre>
<pre><code>1    &#x5C0F;&#x660E;
Name: name, dtype: object
</code></pre><pre><code class="lang-python">a[<span class="hljs-string">&apos;name&apos;</span>][<span class="hljs-number">0</span>:<span class="hljs-number">1</span>]
</code></pre>
<pre><code>1    &#x5C0F;&#x660E;
Name: name, dtype: object
</code></pre>
                    
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